ffs-deprecated: Forward feature selection

In environmentalinformatics-marburg/Rsenal: Magic R Functions for Things Various

Forward feature selection

Description

A simple forward feature selection algorithm

Usage

ffs(predictors, response, method = "rf"
, metric = ifelse(is.factor(response), "Accuracy", "RMSE")
, maximize = ifelse(metric == "RMSE", FALSE, TRUE), withinSD = FALSE
, trControl = trainControl(), tuneLength = 3, tuneGrid = NULL
, seed = sample(1:1000, 1), runParallel = FALSE, ...)

Arguments

predictors	see train
response	see train
method	see train
metric	see train
maximize	see train
withinSD	Logical Models are only selected if they are better than the currently best models Standard error
trControl	see train
tuneLength	see train
tuneGrid	see train
seed	A random number
runParallel	Logical
...	arguments passed to the classification or regression routine (such as randomForest). Errors will occur if values for tuning parameters are passed here.

Details

Models with two predictors are first trained using all possible pairs of predictor variables. The best model of these initial models is kept. On the basis of this best model the predictor variables are iteratively increased and each of the remaining variables is tested for its improvement of the currently best model. The process stops if none of the remaining variables increases the model performance when added to the current best model.

The internal cross validation can be run in parallel. See information on parallel processing of carets train functions for details.

Using withinSE will favour models with less variables and probably shorten the calculation time

Value

A list of class train. Beside of the usual train contentm the object contains the vector "selectedvars" and "selectedvars_perf" that give the order of the variables selected as well as their corresponding performance (starting from the first two variables)

Note

This validation is particulary suitable for leave-one-station-out cross validations where variable selection MUST be based on the performance of the model on the hold out station. A computational time expesnsive alternative is the best subset selection (bss).

Author(s)

Hanna Meyer

See Also

train, bss, trainControl,rfe

Rsenal-deprecated

Examples

 ## Not run: 
data(iris)
ffsmodel <- ffs(iris[,1:4],iris$Species)
ffsmodel$selectedvars
ffsmodel$selectedvars_perf 

## End(Not run)

environmentalinformatics-marburg/Rsenal documentation built on July 28, 2023, 6:09 a.m.